OpenRaiser/Intern-Atlas huggingface dataset:
G-Fuzz: A Directed Fuzzing Framework for gVisor
Venue: arXiv cs.CR 2024
Year: 2024

gVisor is a Google-published application-level kernel for containers. As gVisor is lightweight and has sound isolation, it has been widely used in many IT enterprises \cite{Stripe, DigitalOcean, Cloundflare}. When a new vulnerability of the upstream gVisor is found, it is important for the downstream developers to test the corresponding code to maintain the security. To achieve this aim, directed fuzzing is promising. Nevertheless, there are many challenges in applying existing directed fuzzing methods for gVisor. The core reason is that existing directed fuzzers are mainly for general C/C++ applications, while gVisor is an OS kernel written in the Go language. To address the above challenges, we propose G-Fuzz, a directed fuzzing framework for gVisor. There are three core methods in G-Fuzz, including lightweight and fine-grained distance calculation, target related syscall inference and utilization, and exploration and exploitation dynamic switch. Note that the methods of G-Fuzz are general and can be transferred to other OS kernels. We conduct extensive experiments to evaluate the performance of G-Fuzz. Compared to Syzkaller, the state-of-the-art kernel fuzzer, G-Fuzz outperforms it significantly. Furthermore, we have rigorously evaluated the importance for each core method of G-Fuzz. G-Fuzz has been deployed in industry and has detected multiple serious vulnerabilities.
Stabl: Blockchain Fault Tolerance
Venue: arXiv cs.DC 2024
Year: 2024

Blockchain promises to make online services more fault tolerant due to their inherent distributed nature. Their ability to execute arbitrary programs in different geo-distributed regions and on diverse operating systems make them an alternative of choice to our dependence on unique software whose recent failure affected 8.5 millions of machines. As of today, it remains, however, unclear whether blockchains can truly tolerate failures. In this paper, we assess the fault tolerance of blockchain. To this end, we inject failures in controlled deployments of five modern blockchain systems, namely Algorand, Aptos, Avalanche, Redbelly and Solana. We introduce a novel sensitivity metric, interesting in its own right, as the difference between the integrals of two cumulative distribution functions, one obtained in a baseline environment and one obtained in an adversarial environment. Our results indicate that (i) all blockchains except Redbelly are highly impacted by the failure of a small part of their network, (ii) Avalanche and Redbelly benefit from the redundant information needed for Byzantine fault tolerance while others are hampered by it, and more dramatically (iii) Avalanche and Solana cannot recover from localised transient failures.
Score-Based Multibeam Point Cloud Denoising
Venue: arXiv cs.LG 2024
Year: 2024

Multibeam echo-sounder (MBES) is the de-facto sensor for bathymetry mapping. In recent years, cheaper MBES sensors and global mapping initiatives have led to exponential growth of available data. However, raw MBES data contains 1-25% of noise that requires semi-automatic filtering using tools such as Combined Uncertainty and Bathymetric Estimator (CUBE). In this work, we draw inspirations from the 3D point cloud community and adapted a score-based point cloud denoising network for MBES outlier detection and denoising. We trained and evaluated this network on real MBES survey data. The proposed method was found to outperform classical methods, and can be readily integrated into existing MBES standard workflow. To facilitate future research, the code and pretrained model are available online.
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework
Venue: arXiv cs.CV 2024
Year: 2024

Detecting anomalies within point clouds is crucial for various industrial applications, but traditional unsupervised methods face challenges due to data acquisition costs, early-stage production constraints, and limited generalization across product categories. To overcome these challenges, we introduce the Multi-View Projection (MVP) framework, leveraging pre-trained Vision-Language Models (VLMs) to detect anomalies. Specifically, MVP projects point cloud data into multi-view depth images, thereby translating point cloud anomaly detection into image anomaly detection. Following zero-shot image anomaly detection methods, pre-trained VLMs are utilized to detect anomalies on these depth images. Given that pre-trained VLMs are not inherently tailored for zero-shot point cloud anomaly detection and may lack specificity, we propose the integration of learnable visual and adaptive text prompting techniques to fine-tune these VLMs, thereby enhancing their detection performance. Extensive experiments on the MVTec 3D-AD and Real3D-AD demonstrate our proposed MVP framework's superior zero-shot anomaly detection performance and the prompting techniques' effectiveness. Real-world evaluations on automotive plastic part inspection further showcase that the proposed method can also be generalized to practical unseen scenarios. The code is available at https://github.com/hustCYQ/MVP-PCLIP.
An Adaptive End-to-End IoT Security Framework Using Explainable AI and LLMs
Venue: arXiv cs.LG 2024
Year: 2024

The exponential growth of the Internet of Things (IoT) has significantly increased the complexity and volume of cybersecurity threats, necessitating the development of advanced, scalable, and interpretable security frameworks. This paper presents an innovative, comprehensive framework for real-time IoT attack detection and response that leverages Machine Learning (ML), Explainable AI (XAI), and Large Language Models (LLM). By integrating XAI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) with a model-independent architecture, we ensure our framework's adaptability across various ML algorithms. Additionally, the incorporation of LLMs enhances the interpretability and accessibility of detection decisions, providing system administrators with actionable, human-understandable explanations of detected threats. Our end-to-end framework not only facilitates a seamless transition from model development to deployment but also represents a real-world application capability that is often lacking in existing research. Based on our experiments with the CIC-IOT-2023 dataset \cite{neto2023ciciot2023}, Gemini and OPENAI LLMS demonstrate unique strengths in attack mitigation: Gemini offers precise, focused strategies, while OPENAI provides extensive, in-depth security measures. Incorporating SHAP and LIME algorithms within XAI provides comprehensive insights into attack detection, emphasizing opportunities for model improvement through detailed feature analysis, fine-tuning, and the adaptation of misclassifications to enhance accuracy.
A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How
Venue: arXiv cs.SE 2024
Year: 2024

API suggestion is a critical task in modern software development, assisting programmers by predicting and recommending third-party APIs based on the current context. Recent advancements in large code models (LCMs) have shown promise in the API suggestion task. However, they mainly focus on suggesting which APIs to use, ignoring that programmers may demand more assistance while using APIs in practice including when to use the suggested APIs and how to use the APIs. To mitigate the gap, we conduct a systematic evaluation of LCMs for the API suggestion task in the paper. To facilitate our investigation, we first build a benchmark that contains a diverse collection of code snippets, covering 176 APIs used in 853 popular Java projects. Three distinct scenarios in the API suggestion task are then considered for evaluation, including (1) ``\textit{when to use}'', which aims at determining the desired position and timing for API usage; (2) ``\textit{which to use}'', which aims at identifying the appropriate API from a given library; and (3) ``\textit{how to use}'', which aims at predicting the arguments for a given API. The consideration of the three scenarios allows for a comprehensive assessment of LCMs' capabilities in suggesting APIs for developers. During the evaluation, we choose nine popular LCMs with varying model sizes for the three scenarios. We also perform an in-depth analysis of the influence of context selection on the model performance ...
Balancing Label Imbalance in Federated Environments Using Only Mixup and Artificially-Labeled Noise
Venue: arXiv cs.LG 2024
Year: 2024

Clients in a distributed or federated environment will often hold data skewed towards differing subsets of labels. This scenario, referred to as heterogeneous or non-iid federated learning, has been shown to significantly hinder model training and performance. In this work, we explore the limits of a simple yet effective augmentation strategy for balancing skewed label distributions: filling in underrepresented samples of a particular label class using pseudo-images. While existing algorithms exclusively train on pseudo-images such as mixups of local training data, our augmented client datasets consist of both real and pseudo-images. In further contrast to other literature, we (1) use a DP-Instahide variant to reduce the decodability of our image encodings and (2) as a twist, supplement local data using artificially labeled, training-free 'natural noise' generated by an untrained StyleGAN. These noisy images mimic the power spectra patterns present in natural scenes which, together with mixup images, help homogenize label distribution among clients. We demonstrate that small amounts of augmentation via mixups and natural noise markedly improve label-skewed CIFAR-10 and MNIST training.
T2M-X: Learning Expressive Text-to-Motion Generation from Partially Annotated Data
Venue: arXiv cs.CV 2024
Year: 2024

The generation of humanoid animation from text prompts can profoundly impact animation production and AR/VR experiences. However, existing methods only generate body motion data, excluding facial expressions and hand movements. This limitation, primarily due to a lack of a comprehensive whole-body motion dataset, inhibits their readiness for production use. Recent attempts to create such a dataset have resulted in either motion inconsistency among different body parts in the artificially augmented data or lower quality in the data extracted from RGB videos. In this work, we propose T2M-X, a two-stage method that learns expressive text-to-motion generation from partially annotated data. T2M-X trains three separate Vector Quantized Variational AutoEncoders (VQ-VAEs) for body, hand, and face on respective high-quality data sources to ensure high-quality motion outputs, and a Multi-indexing Generative Pretrained Transformer (GPT) model with motion consistency loss for motion generation and coordination among different body parts. Our results show significant improvements over the baselines both quantitatively and qualitatively, demonstrating its robustness against the dataset limitations.
Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network
Venue: arXiv cs.LG 2024
Year: 2024

Inductive spatial temporal prediction can generalize historical data to predict unseen data, crucial for highly dynamic scenarios (e.g., traffic systems, stock markets). However, external events (e.g., urban structural growth, market crash) and emerging new entities (e.g., locations, stocks) can undermine prediction accuracy by inducing data drift over time. Most existing studies extract invariant patterns to counter data drift but ignore pattern diversity, exhibiting poor generalization to unseen entities. To address this issue, we design an Informative Graph Neural Network (INF-GNN) to distill diversified invariant patterns and improve prediction accuracy under data drift. Firstly, we build an informative subgraph with a uniquely designed metric, Relation Importance (RI), that can effectively select stable entities and distinct spatial relationships. This subgraph further generalizes new entities' data via neighbors merging. Secondly, we propose an informative temporal memory buffer to help the model emphasize valuable timestamps extracted using influence functions within time intervals. This memory buffer allows INF-GNN to discern influential temporal patterns. Finally, RI loss optimization is designed for pattern consolidation. Extensive experiments on real-world dataset under substantial data drift demonstrate that INF-GNN significantly outperforms existing alternatives.
Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts
Venue: arXiv cs.CL 2024
Year: 2024

Adapting keyphrase generation models to new domains typically involves few-shot fine-tuning with in-domain labeled data. However, annotating documents with keyphrases is often prohibitively expensive and impractical, requiring expert annotators. This paper presents silk, an unsupervised method designed to address this issue by extracting silver-standard keyphrases from citation contexts to create synthetic labeled data for domain adaptation. Extensive experiments across three distinct domains demonstrate that our method yields high-quality synthetic samples, resulting in significant and consistent improvements in in-domain performance over strong baselines.
Distributed Control for 3D Inspection using Multi-UAV Systems
Venue: arXiv cs.RO 2024
Year: 2024

Cooperative control of multi-UAV systems has attracted substantial research attention due to its significance in various application sectors such as emergency response, search and rescue missions, and critical infrastructure inspection. This paper proposes a distributed control algorithm to generate collision-free trajectories that drive the multi-UAV system to completely inspect a set of 3D points on the surface of an object of interest. The objective of the UAVs is to cooperatively inspect the object of interest in the minimum amount of time. Extensive numerical simulations for a team of quadrotor UAVs inspecting a real 3D structure illustrate the validity and effectiveness of the proposed approach.
MeMoir: A Software-Driven Covert Channel based on Memory Usage
Venue: arXiv cs.CR 2024
Year: 2024

Covert channel attacks have been continuously studied as severe threats to modern computing systems. Software-based covert channels are a typically hard-to-detect branch of these attacks, since they leverage virtual resources to establish illegitimate communication between malicious actors. In this work, we present MeMoir: a novel software-driven covert channel that, for the first time, utilizes memory usage as the medium for the channel. We implemented the new covert channel on two real-world platforms with different architectures: a general-purpose Intel x86-64-based desktop computer and an ARM64-based embedded system. Our results show that our new architecture- and hardware-agnostic covert channel is effective and achieves moderate transmission rates with very low error. Moreover, we present a real use-case for our attack where we were able to communicate information from a Hyper-V virtualized enviroment to a Windows 11 host system. In addition, we implement a machine learning-based detector that can predict whether an attack is present in the system with an accuracy of more than 95% with low false positive and false negative rates by monitoring the use of system memory. Finally, we introduce a noise-based countermeasure that effectively mitigates the attack while inducing a low power overhead in the system compared to other normal applications.
GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification
Venue: arXiv cs.CL 2024
Year: 2024

Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on text classification tasks with their powerful word embeddings, but their black-box nature, which leads to a lack of interpretability, has been a major concern. In this work, we introduce GAProtoNet, a novel white-box Multi-head Graph Attention-based Prototypical Network designed to explain the decisions of text classification models built with LM encoders. In our approach, the input vector and prototypes are regarded as nodes within a graph, and we utilize multi-head graph attention to selectively construct edges between the input node and prototype nodes to learn an interpretable prototypical representation. During inference, the model makes decisions based on a linear combination of activated prototypes weighted by the attention score assigned for each prototype, allowing its choices to be transparently explained by the attention weights and the prototypes projected into the closest matching training examples. Experiments on multiple public datasets show our approach achieves superior results without sacrificing the accuracy of the original black-box LMs. We also compare with four alternative prototypical network variations and our approach achieves the best accuracy and F1 among all. Our case study and visualization of prototype clusters also demonstrate the efficiency in explaining the decisions of black-box models built with LMs.
JMedBench: A Benchmark for Evaluating Japanese Biomedical Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Recent developments in Japanese large language models (LLMs) primarily focus on general domains, with fewer advancements in Japanese biomedical LLMs. One obstacle is the absence of a comprehensive, large-scale benchmark for comparison. Furthermore, the resources for evaluating Japanese biomedical LLMs are insufficient. To advance this field, we propose a new benchmark including eight LLMs across four categories and 20 Japanese biomedical datasets across five tasks. Experimental results indicate that: (1) LLMs with a better understanding of Japanese and richer biomedical knowledge achieve better performance in Japanese biomedical tasks, (2) LLMs that are not mainly designed for Japanese biomedical domains can still perform unexpectedly well, and (3) there is still much room for improving the existing LLMs in certain Japanese biomedical tasks. Moreover, we offer insights that could further enhance development in this field. Our evaluation tools tailored to our benchmark as well as the datasets are publicly available in https://huggingface.co/datasets/Coldog2333/JMedBench to facilitate future research.
Generative Aerodynamic Design with Diffusion Probabilistic Models
Venue: arXiv cs.CE 2024
Year: 2024

The optimization of geometries for aerodynamic design often relies on a large number of expensive simulations to evaluate and iteratively improve the geometries. It is possible to reduce the number of simulations by providing a starting geometry that has properties close to the desired requirements, often in terms of lift and drag, aerodynamic moments and surface areas. We show that generative models have the potential to provide such starting geometries by generalizing geometries over a large dataset of simulations. In particular, we leverage diffusion probabilistic models trained on XFOIL simulations to synthesize two-dimensional airfoil geometries conditioned on given aerodynamic features and constraints. The airfoils are parameterized with Bernstein polynomials, ensuring smoothness of the generated designs. We show that the models are able to generate diverse candidate designs for identical requirements and constraints, effectively exploring the design space to provide multiple starting points to optimization procedures. However, the quality of the candidate designs depends on the distribution of the simulated designs in the dataset. Importantly, the geometries in this dataset must satisfy other requirements and constraints that are not used in conditioning of the diffusion model, to ensure that the generated geometries are physical.
V-Hands: Touchscreen-based Hand Tracking for Remote Whiteboard Interaction
Venue: arXiv cs.HC 2024
Year: 2024

In whiteboard-based remote communication, the seamless integration of drawn content and hand-screen interactions is essential for an immersive user experience. Previous methods either require bulky device setups for capturing hand gestures or fail to accurately track the hand poses from capacitive images. In this paper, we present a real-time method for precise tracking 3D poses of both hands from capacitive video frames. To this end, we develop a deep neural network to identify hands and infer hand joint positions from capacitive frames, and then recover 3D hand poses from the hand-joint positions via a constrained inverse kinematic solver. Additionally, we design a device setup for capturing high-quality hand-screen interaction data and obtained a more accurate synchronized capacitive video and hand pose dataset. Our method improves the accuracy and stability of 3D hand tracking for capacitive frames while maintaining a compact device setup for remote communication. We validate our scheme design and its superior performance on 3D hand pose tracking and demonstrate the effectiveness of our method in whiteboard-based remote communication. Our code, model, and dataset are available at https://V-Hands.github.io.
Hey Robot! Personalizing Robot Navigation through Model Predictive Control with a Large Language Model
Venue: arXiv cs.RO 2024
Year: 2024

Robot navigation methods allow mobile robots to operate in applications such as warehouses or hospitals. While the environment in which the robot operates imposes requirements on its navigation behavior, most existing methods do not allow the end-user to configure the robot's behavior and priorities, possibly leading to undesirable behavior (e.g., fast driving in a hospital). We propose a novel approach to adapt robot motion behavior based on natural language instructions provided by the end-user. Our zero-shot method uses an existing Visual Language Model to interpret a user text query or an image of the environment. This information is used to generate the cost function and reconfigure the parameters of a Model Predictive Controller, translating the user's instruction to the robot's motion behavior. This allows our method to safely and effectively navigate in dynamic and challenging environments. We extensively evaluate our method's individual components and demonstrate the effectiveness of our method on a ground robot in simulation and real-world experiments, and across a variety of environments and user specifications.
Unsourced Sparse Multiple Access foUnsourced Sparse Multiple Access for 6G Massive Communicationr 6G Massive Communication
Venue: arXiv cs.IT 2024
Year: 2024

Massive communication is one of key scenarios of 6G where two magnitude higher connection density would be required to serve diverse services. As a promising direction, unsourced multiple access has been proved to outperform significantly over orthogonal multiple access (OMA) or slotted-ALOHA in massive connections. In this paper we describe a design framework of unsourced sparse multiple access (USMA) that consists of two key modules: compressed sensing for preamble generation, and sparse interleaver division multiple access (SIDMA) for main packet transmission. Simulation results of general design of USMA show that the theoretical bound can be approached within 1~1.5 dB by using simple channel codes like convolutional. To illustrate the scalability of USMA, a customized design for ambient Internet of Things (A-IoT) is proposed, so that much less memory and computation are required. Simulations results of Rayleigh fading and realistic channel estimation show that USMA based A-IoT solution can deliver nearly 4 times capacity and 6 times efficiency for random access over traditional radio frequency identification (RFID) technology.